PerceptronLayer Class Reference

This class represents a layer of perceptrons. More...

#include <perceptron_layer.h>

Inheritance diagram for PerceptronLayer:
Layer

Public Types

enum class  ActivationFunction {
  Threshold , SymmetricThreshold , Logistic , HyperbolicTangent ,
  Linear , RectifiedLinear , ExponentialLinear , ScaledExponentialLinear ,
  SoftPlus , SoftSign , HardSigmoid
}
 Enumeration of available activation functions for the perceptron neuron model. More...
 
- Public Types inherited from Layer
enum class  Type {
  Scaling , Convolutional , Perceptron , Pooling ,
  Probabilistic , LongShortTermMemory , Recurrent , Unscaling ,
  Bounding
}
 This enumeration represents the possible types of layers. More...
 

Public Member Functions

 PerceptronLayer ()
 
 PerceptronLayer (const Index &, const Index &, const ActivationFunction &=PerceptronLayer::ActivationFunction::HyperbolicTangent)
 
virtual ~PerceptronLayer ()
 
bool is_empty () const
 
Index get_inputs_number () const
 Returns the number of inputs to the layer. More...
 
Index get_neurons_number () const
 Returns the number of neurons in the layer. More...
 
const Tensor< type, 2 > & get_biases () const
 
const Tensor< type, 2 > & get_synaptic_weights () const
 
Tensor< type, 2 > get_biases (const Tensor< type, 1 > &) const
 
Tensor< type, 2 > get_synaptic_weights (const Tensor< type, 1 > &) const
 
Index get_biases_number () const
 
Index get_synaptic_weights_number () const
 Returns the number of layer's synaptic weights. More...
 
Index get_parameters_number () const
 Returns the number of parameters(biases and synaptic weights) of the layer. More...
 
Tensor< type, 1 > get_parameters () const
 
const PerceptronLayer::ActivationFunctionget_activation_function () const
 
string write_activation_function () const
 
const bool & get_display () const
 
void set ()
 
void set (const Index &, const Index &, const PerceptronLayer::ActivationFunction &=PerceptronLayer::ActivationFunction::HyperbolicTangent)
 
void set_default ()
 
void set_name (const string &)
 
void set_inputs_number (const Index &)
 
void set_neurons_number (const Index &)
 
void set_biases (const Tensor< type, 2 > &)
 
void set_synaptic_weights (const Tensor< type, 2 > &)
 
void set_parameters (const Tensor< type, 1 > &, const Index &index=0)
 Sets the parameters of this layer. More...
 
void set_activation_function (const ActivationFunction &)
 
void set_activation_function (const string &)
 
void set_display (const bool &)
 
void set_biases_constant (const type &)
 
void set_synaptic_weights_constant (const type &)
 
void set_parameters_constant (const type &)
 
void set_parameters_random ()
 
void calculate_combinations (const Tensor< type, 2 > &, const Tensor< type, 2 > &, const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void calculate_activations (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void calculate_activations_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
Tensor< type, 2 > calculate_outputs (const Tensor< type, 2 > &)
 
void forward_propagate (const Tensor< type, 2 > &, LayerForwardPropagation *)
 
void forward_propagate (const Tensor< type, 2 > &, Tensor< type, 1 >, LayerForwardPropagation *)
 
void calculate_hidden_delta (LayerForwardPropagation *, LayerBackPropagation *, LayerForwardPropagation *, LayerBackPropagation *) const
 
void calculate_hidden_delta_perceptron (PerceptronLayerForwardPropagation *, PerceptronLayerBackPropagation *, PerceptronLayerBackPropagation *) const
 
void calculate_hidden_delta_probabilistic (ProbabilisticLayerForwardPropagation *, ProbabilisticLayerBackPropagation *, PerceptronLayerBackPropagation *) const
 
void calculate_hidden_delta_lm (LayerForwardPropagation *, LayerBackPropagationLM *, LayerBackPropagationLM *) const
 
void calculate_hidden_delta_perceptron_lm (PerceptronLayerForwardPropagation *, PerceptronLayerBackPropagationLM *, PerceptronLayerBackPropagationLM *) const
 
void calculate_hidden_delta_probabilistic_lm (ProbabilisticLayerForwardPropagation *, ProbabilisticLayerBackPropagationLM *, PerceptronLayerBackPropagationLM *) const
 
void calculate_squared_errors_Jacobian_lm (const Tensor< type, 2 > &, LayerForwardPropagation *, LayerBackPropagationLM *)
 
void insert_squared_errors_Jacobian_lm (LayerBackPropagationLM *, const Index &, Tensor< type, 2 > &) const
 
void calculate_error_gradient (const Tensor< type, 2 > &, LayerForwardPropagation *, LayerBackPropagation *) const
 
void insert_gradient (LayerBackPropagation *, const Index &, Tensor< type, 1 > &) const
 
string write_expression (const Tensor< string, 1 > &, const Tensor< string, 1 > &) const
 
string write_activation_function_expression () const
 
string write_expression_c () const
 
string write_combinations_c () const
 
string write_activations_c () const
 
string write_combinations_python () const
 
string write_activations_python () const
 
string write_expression_python () const
 
void from_XML (const tinyxml2::XMLDocument &)
 
void write_XML (tinyxml2::XMLPrinter &) const
 
- Public Member Functions inherited from Layer
string get_name () const
 
void set_threads_number (const int &)
 
virtual Tensor< type, 2 > calculate_outputs_from4D (const Tensor< type, 4 > &)
 
virtual Tensor< type, 4 > calculate_outputs_4d (const Tensor< type, 4 > &)
 
virtual void forward_propagate (const Tensor< type, 4 > &, LayerForwardPropagation *)
 
virtual void forward_propagate (const Tensor< type, 4 > &, Tensor< type, 1 >, LayerForwardPropagation *)
 
virtual void calculate_error_gradient (const Tensor< type, 4 > &, LayerForwardPropagation *, LayerBackPropagation *) const
 
Type get_type () const
 
string get_type_string () const
 Takes the type of layer used by the model. More...
 

Protected Attributes

Tensor< type, 2 > biases
 
Tensor< type, 2 > synaptic_weights
 This matrix containing conection strengths from a layer's inputs to its neurons. More...
 
ActivationFunction activation_function
 Activation function variable. More...
 
bool display = true
 Display messages to screen. More...
 
- Protected Attributes inherited from Layer
ThreadPool * thread_pool = nullptr
 
ThreadPoolDevice * thread_pool_device = nullptr
 
string layer_name = "layer"
 Layer name. More...
 
Type layer_type = Type::Perceptron
 Layer type. More...
 
const Eigen::array< IndexPair< Index >, 1 > A_BT = {IndexPair<Index>(1, 1)}
 
const Eigen::array< IndexPair< Index >, 1 > AT_B = {IndexPair<Index>(0, 0)}
 
const Eigen::array< IndexPair< Index >, 1 > A_B = {IndexPair<Index>(1, 0)}
 

Additional Inherited Members

- Protected Member Functions inherited from Layer
void hard_sigmoid (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void hyperbolic_tangent (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void logistic (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void threshold (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void symmetric_threshold (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void rectified_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void scaled_exponential_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void soft_plus (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void soft_sign (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void exponential_linear (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void softmax (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void binary (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void competitive (const Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void hard_sigmoid_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void hyperbolic_tangent_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void logistic_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void threshold_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void symmetric_threshold_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void rectified_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void scaled_exponential_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void soft_plus_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void soft_sign_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void exponential_linear_derivatives (const Tensor< type, 1 > &, Tensor< type, 1 > &, Tensor< type, 1 > &) const
 
void hard_sigmoid (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void hyperbolic_tangent (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void logistic (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void symmetric_threshold (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void rectified_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void scaled_exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void soft_plus (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void soft_sign (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void exponential_linear (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void softmax (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void binary (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void competitive (const Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void hard_sigmoid_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void hyperbolic_tangent_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void symmetric_threshold_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void rectified_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void scaled_exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void soft_plus_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void soft_sign_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void exponential_linear_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 2 > &) const
 
void logistic_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const
 
void softmax_derivatives (const Tensor< type, 2 > &, Tensor< type, 2 > &, Tensor< type, 3 > &) const
 
void linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void logistic (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void hyperbolic_tangent (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void symmetric_threshold (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void rectified_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void scaled_exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void soft_plus (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void soft_sign (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void hard_sigmoid (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void exponential_linear (const Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void logistic_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void hyperbolic_tangent_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void symmetric_threshold_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void rectified_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void scaled_exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void soft_plus_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void soft_sign_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void hard_sigmoid_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 
void exponential_linear_derivatives (const Tensor< type, 4 > &, Tensor< type, 4 > &, Tensor< type, 4 > &) const
 

Detailed Description

This class represents a layer of perceptrons.

PerceptronLayer is a single-layer network with a hard-limit trabsfer function. This network is often trained with the perceptron learning rule.

Layers of perceptrons will be used to construct multilayer perceptrons, such as an approximation problems .

Definition at line 48 of file perceptron_layer.h.

Member Enumeration Documentation

◆ ActivationFunction

enum class ActivationFunction
strong

Enumeration of available activation functions for the perceptron neuron model.

Definition at line 56 of file perceptron_layer.h.

Constructor & Destructor Documentation

◆ PerceptronLayer() [1/2]

PerceptronLayer ( )
explicit

Default constructor. It creates a empty layer object, with no perceptrons. This constructor also initializes the rest of class members to their default values.

Definition at line 18 of file perceptron_layer.cpp.

◆ PerceptronLayer() [2/2]

PerceptronLayer ( const Index &  new_inputs_number,
const Index &  new_neurons_number,
const ActivationFunction new_activation_function = PerceptronLayer::ActivationFunction::HyperbolicTangent 
)
explicit

Layer architecture constructor. It creates a layer object with given numbers of inputs and perceptrons. The parameters are initialized at random. This constructor also initializes the rest of class members to their default values.

Parameters
new_inputs_numberNumber of inputs in the layer.
new_neurons_numberNumber of perceptrons in the layer.
new_activation_functionActivation function for the perceptron layer.

Definition at line 34 of file perceptron_layer.cpp.

◆ ~PerceptronLayer()

~PerceptronLayer ( )
virtual

Destructor. This destructor does not delete any pointer.

Definition at line 48 of file perceptron_layer.cpp.

Member Function Documentation

◆ calculate_activations()

void calculate_activations ( const Tensor< type, 2 > &  combinations,
Tensor< type, 2 > &  activations 
) const

Definition at line 515 of file perceptron_layer.cpp.

◆ calculate_activations_derivatives()

void calculate_activations_derivatives ( const Tensor< type, 2 > &  combinations,
Tensor< type, 2 > &  activations,
Tensor< type, 2 > &  activations_derivatives 
) const

Definition at line 549 of file perceptron_layer.cpp.

◆ calculate_combinations()

void calculate_combinations ( const Tensor< type, 2 > &  inputs,
const Tensor< type, 2 > &  biases,
const Tensor< type, 2 > &  synaptic_weights,
Tensor< type, 2 > &  combinations 
) const

Definition at line 491 of file perceptron_layer.cpp.

◆ calculate_error_gradient()

void calculate_error_gradient ( const Tensor< type, 2 > &  inputs,
LayerForwardPropagation forward_propagation,
LayerBackPropagation back_propagation 
) const
virtual

Reimplemented from Layer.

Definition at line 988 of file perceptron_layer.cpp.

◆ calculate_hidden_delta()

void calculate_hidden_delta ( LayerForwardPropagation next_layer_forward_propagation,
LayerBackPropagation next_layer_back_propagation,
LayerForwardPropagation layer_forward_propagation,
LayerBackPropagation layer_back_propagation 
) const
virtual

Reimplemented from Layer.

Definition at line 657 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_lm()

void calculate_hidden_delta_lm ( LayerForwardPropagation next_layer_forward_propagation,
LayerBackPropagationLM next_layer_back_propagation,
LayerBackPropagationLM layer_back_propagation 
) const
virtual

Reimplemented from Layer.

Definition at line 807 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_perceptron()

void calculate_hidden_delta_perceptron ( PerceptronLayerForwardPropagation next_forward_propagation,
PerceptronLayerBackPropagation next_back_propagation,
PerceptronLayerBackPropagation back_propagation 
) const

Definition at line 707 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_perceptron_lm()

void calculate_hidden_delta_perceptron_lm ( PerceptronLayerForwardPropagation next_forward_propagation,
PerceptronLayerBackPropagationLM next_back_propagation,
PerceptronLayerBackPropagationLM back_propagation 
) const

Definition at line 849 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_probabilistic()

void calculate_hidden_delta_probabilistic ( ProbabilisticLayerForwardPropagation next_forward_propagation,
ProbabilisticLayerBackPropagation next_back_propagation,
PerceptronLayerBackPropagation back_propagation 
) const

Definition at line 722 of file perceptron_layer.cpp.

◆ calculate_hidden_delta_probabilistic_lm()

void calculate_hidden_delta_probabilistic_lm ( ProbabilisticLayerForwardPropagation next_forward_propagation,
ProbabilisticLayerBackPropagationLM next_back_propagation,
PerceptronLayerBackPropagationLM back_propagation 
) const

Definition at line 860 of file perceptron_layer.cpp.

◆ calculate_outputs()

Tensor< type, 2 > calculate_outputs ( const Tensor< type, 2 > &  inputs)
virtual

Reimplemented from Layer.

Definition at line 586 of file perceptron_layer.cpp.

◆ calculate_squared_errors_Jacobian_lm()

void calculate_squared_errors_Jacobian_lm ( const Tensor< type, 2 > &  inputs,
LayerForwardPropagation forward_propagation,
LayerBackPropagationLM back_propagation 
)
virtual

Reimplemented from Layer.

Definition at line 931 of file perceptron_layer.cpp.

◆ forward_propagate() [1/2]

void forward_propagate ( const Tensor< type, 2 > &  inputs,
LayerForwardPropagation forward_propagation 
)
virtual

Reimplemented from Layer.

Definition at line 605 of file perceptron_layer.cpp.

◆ forward_propagate() [2/2]

void forward_propagate ( const Tensor< type, 2 > &  inputs,
Tensor< type, 1 >  potential_parameters,
LayerForwardPropagation forward_propagation 
)
virtual

Reimplemented from Layer.

Definition at line 626 of file perceptron_layer.cpp.

◆ from_XML()

void from_XML ( const tinyxml2::XMLDocument document)
virtual

Reimplemented from Layer.

Definition at line 1065 of file perceptron_layer.cpp.

◆ get_activation_function()

const PerceptronLayer::ActivationFunction & get_activation_function ( ) const

Returns the activation function of the layer. The activation function of a layer is the activation function of all perceptrons in it.

Definition at line 172 of file perceptron_layer.cpp.

◆ get_biases() [1/2]

const Tensor< type, 2 > & get_biases ( ) const

Returns the biases from all the perceptrons in the layer. The format is a vector of real values. The size of this vector is the number of neurons in the layer.

Definition at line 95 of file perceptron_layer.cpp.

◆ get_biases() [2/2]

Tensor< type, 2 > get_biases ( const Tensor< type, 1 > &  parameters) const

Definition at line 132 of file perceptron_layer.cpp.

◆ get_biases_number()

Index get_biases_number ( ) const

Definition at line 69 of file perceptron_layer.cpp.

◆ get_display()

const bool & get_display ( ) const

Returns true if messages from this class are to be displayed on the screen, or false if messages from this class are not to be displayed on the screen.

Definition at line 226 of file perceptron_layer.cpp.

◆ get_inputs_number()

Index get_inputs_number ( ) const
virtual

Returns the number of inputs to the layer.

Reimplemented from Layer.

Definition at line 55 of file perceptron_layer.cpp.

◆ get_neurons_number()

Index get_neurons_number ( ) const
virtual

Returns the number of neurons in the layer.

Reimplemented from Layer.

Definition at line 63 of file perceptron_layer.cpp.

◆ get_parameters()

Tensor< type, 1 > get_parameters ( ) const
virtual

Returns a single vector with all the layer parameters. The format is a vector of real values. The size is the number of parameters in the layer.

Reimplemented from Layer.

Definition at line 151 of file perceptron_layer.cpp.

◆ get_parameters_number()

Index get_parameters_number ( ) const
virtual

Returns the number of parameters(biases and synaptic weights) of the layer.

Reimplemented from Layer.

Definition at line 85 of file perceptron_layer.cpp.

◆ get_synaptic_weights() [1/2]

const Tensor< type, 2 > & get_synaptic_weights ( ) const

Returns the synaptic weights from the perceptrons. The format is a matrix of real values. The number of rows is the number of neurons in the layer. The number of columns is the number of inputs to the layer.

Definition at line 106 of file perceptron_layer.cpp.

◆ get_synaptic_weights() [2/2]

Tensor< type, 2 > get_synaptic_weights ( const Tensor< type, 1 > &  parameters) const

Definition at line 112 of file perceptron_layer.cpp.

◆ get_synaptic_weights_number()

Index get_synaptic_weights_number ( ) const
virtual

Returns the number of layer's synaptic weights.

Reimplemented from Layer.

Definition at line 77 of file perceptron_layer.cpp.

◆ insert_gradient()

void insert_gradient ( LayerBackPropagation back_propagation,
const Index &  index,
Tensor< type, 1 > &  gradient 
) const
virtual

Reimplemented from Layer.

Definition at line 1013 of file perceptron_layer.cpp.

◆ insert_squared_errors_Jacobian_lm()

void insert_squared_errors_Jacobian_lm ( LayerBackPropagationLM back_propagation,
const Index &  index,
Tensor< type, 2 > &  squared_errors_Jacobian 
) const
virtual

Reimplemented from Layer.

Definition at line 972 of file perceptron_layer.cpp.

◆ set() [1/2]

void set ( )

Sets an empty layer, wihtout any perceptron. It also sets the rest of members to their default values.

Definition at line 235 of file perceptron_layer.cpp.

◆ set() [2/2]

void set ( const Index &  new_inputs_number,
const Index &  new_neurons_number,
const PerceptronLayer::ActivationFunction new_activation_function = PerceptronLayer::ActivationFunction::HyperbolicTangent 
)

Sets new numbers of inputs and perceptrons in the layer. It also sets the rest of members to their default values.

Parameters
new_inputs_numberNumber of inputs.
new_neurons_numberNumber of perceptron neurons.
new_activation_functionActivation function for the perceptron layer.

Definition at line 251 of file perceptron_layer.cpp.

◆ set_activation_function() [1/2]

void set_activation_function ( const ActivationFunction new_activation_function)

This class sets a new activation(or transfer) function in a single layer.

Parameters
new_activation_functionActivation function for the layer.

Definition at line 358 of file perceptron_layer.cpp.

◆ set_activation_function() [2/2]

void set_activation_function ( const string &  new_activation_function_name)

Sets a new activation(or transfer) function in a single layer. The second argument is a string containing the name of the function("Logistic", "HyperbolicTangent", "Threshold", etc).

Parameters
new_activation_function_nameActivation function for that layer.

Definition at line 368 of file perceptron_layer.cpp.

◆ set_biases()

void set_biases ( const Tensor< type, 2 > &  new_biases)

Sets the biases of all perceptrons in the layer from a single vector.

Parameters
new_biasesNew set of biases in the layer.

Definition at line 320 of file perceptron_layer.cpp.

◆ set_biases_constant()

void set_biases_constant ( const type &  value)

Initializes the biases of all the perceptrons in the layer of perceptrons with a given value.

Parameters
valueBiases initialization value.

Definition at line 441 of file perceptron_layer.cpp.

◆ set_default()

void set_default ( )

Sets those members not related to the vector of perceptrons to their default value.

  • Display: True.
  • layer_type: Perceptron_Layer.
  • trainable: True.

Definition at line 273 of file perceptron_layer.cpp.

◆ set_display()

void set_display ( const bool &  new_display)

Sets a new display value. If it is set to true messages from this class are to be displayed on the screen; if it is set to false messages from this class are not to be displayed on the screen.

Parameters
new_displayDisplay value.

Definition at line 432 of file perceptron_layer.cpp.

◆ set_inputs_number()

void set_inputs_number ( const Index &  new_inputs_number)
virtual

Sets a new number of inputs in the layer. The new synaptic weights are initialized at random.

Parameters
new_inputs_numberNumber of layer inputs.

Reimplemented from Layer.

Definition at line 293 of file perceptron_layer.cpp.

◆ set_name()

void set_name ( const string &  new_layer_name)

Definition at line 283 of file perceptron_layer.cpp.

◆ set_neurons_number()

void set_neurons_number ( const Index &  new_neurons_number)
virtual

Sets a new number perceptrons in the layer. All the parameters are also initialized at random.

Parameters
new_neurons_numberNew number of neurons in the layer.

Reimplemented from Layer.

Definition at line 307 of file perceptron_layer.cpp.

◆ set_parameters()

void set_parameters ( const Tensor< type, 1 > &  new_parameters,
const Index &  index = 0 
)
virtual

Sets the parameters of this layer.

Reimplemented from Layer.

Definition at line 340 of file perceptron_layer.cpp.

◆ set_parameters_constant()

void set_parameters_constant ( const type &  value)
virtual

Initializes all the biases and synaptic weights in the neural newtork with a given value.

Parameters
valueParameters initialization value.

Reimplemented from Layer.

Definition at line 459 of file perceptron_layer.cpp.

◆ set_parameters_random()

void set_parameters_random ( )
virtual

Initializes all the biases and synaptic weights in the neural newtork at random with values comprised between -1 and +1.

Reimplemented from Layer.

Definition at line 470 of file perceptron_layer.cpp.

◆ set_synaptic_weights()

void set_synaptic_weights ( const Tensor< type, 2 > &  new_synaptic_weights)

Sets the synaptic weights of this perceptron layer from a single matrix. The format is a matrix of real numbers. The number of rows is the number of neurons in the corresponding layer. The number of columns is the number of inputs to the corresponding layer.

Parameters
new_synaptic_weightsNew set of synaptic weights in that layer.

Definition at line 332 of file perceptron_layer.cpp.

◆ set_synaptic_weights_constant()

void set_synaptic_weights_constant ( const type &  value)

Initializes the synaptic weights of all the perceptrons in the layer of perceptrons with a given value.

Parameters
valueSynaptic weights initialization value.

Definition at line 450 of file perceptron_layer.cpp.

◆ write_activation_function()

string write_activation_function ( ) const

Returns a string with the name of the layer activation function. This can be: Logistic, HyperbolicTangent, Threshold, SymmetricThreshold, Linear, RectifiedLinear, ScaledExponentialLinear.

Definition at line 181 of file perceptron_layer.cpp.

◆ write_activation_function_expression()

string write_activation_function_expression ( ) const

Definition at line 1246 of file perceptron_layer.cpp.

◆ write_activations_c()

string write_activations_c ( ) const
Todo:

Definition at line 1313 of file perceptron_layer.cpp.

◆ write_activations_python()

string write_activations_python ( ) const
Todo:

Definition at line 1404 of file perceptron_layer.cpp.

◆ write_combinations_c()

string write_combinations_c ( ) const

Definition at line 1288 of file perceptron_layer.cpp.

◆ write_combinations_python()

string write_combinations_python ( ) const

Definition at line 1377 of file perceptron_layer.cpp.

◆ write_expression()

string write_expression ( const Tensor< string, 1 > &  inputs_names,
const Tensor< string, 1 > &  outputs_names 
) const
virtual

Returns a string with the expression of the inputs-outputs relationship of the layer.

Parameters
inputs_namesvector of strings with the name of the layer inputs.
outputs_namesvector of strings with the name of the layer outputs.

Reimplemented from Layer.

Definition at line 1037 of file perceptron_layer.cpp.

◆ write_expression_c()

string write_expression_c ( ) const
virtual

Reimplemented from Layer.

Definition at line 1469 of file perceptron_layer.cpp.

◆ write_expression_python()

string write_expression_python ( ) const
virtual

Reimplemented from Layer.

Definition at line 1485 of file perceptron_layer.cpp.

◆ write_XML()

void write_XML ( tinyxml2::XMLPrinter file_stream) const
virtual

Reimplemented from Layer.

Definition at line 1176 of file perceptron_layer.cpp.

Member Data Documentation

◆ activation_function

ActivationFunction activation_function
protected

Activation function variable.

Definition at line 248 of file perceptron_layer.h.

◆ biases

Tensor<type, 2> biases
protected

Bias is a neuron parameter that is summed with the neuron's weighted inputs and passed through the neuron's transfer function to generate the neuron's output.

Definition at line 240 of file perceptron_layer.h.

◆ display

bool display = true
protected

Display messages to screen.

Definition at line 252 of file perceptron_layer.h.

◆ synaptic_weights

Tensor<type, 2> synaptic_weights
protected

This matrix containing conection strengths from a layer's inputs to its neurons.

Definition at line 244 of file perceptron_layer.h.


The documentation for this class was generated from the following files: